SMOL: a systemic methodology for ontology learning from heterogeneous sources

摘要

Organizations are demanding an efficacious knowledge management. Consequently, they are increasing their system innovation investments to turn information into useful knowledge for decision making obtained from heterogeneous Knowledge Sources (KSOs) such as databases, documents, and even ontologies. Methodological Resources (MRs) for the required knowledge discovering and recovering purposes have gradually become more elaborated and mature in the framework of Knowledge Engineering. Particularly, in the Ontology Learning (OL) field, there is a lack of integrated and open methodologies that could involve all the optional KSOs. In this sense, a systemic perspective is introduced combining MRs associated to diverse KSOs to improve the quality of an integral and continuous Knowledge Acquisition (KA) process. The main contributions provided by this work are on one hand, a novel Systemic Methodology for OL (SMOL) from heterogeneous KSOs which is applied for a case study and on the other hand, an evaluation of SMOL.